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modify ip_adapter's ImageCrossAttention scale getter and setter

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this new version makes it robust in case mulitple Mulitply-s are inside the Chain (e.g. if the Linear layers are LoRA-ified)
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Laurent 2024-03-26 09:49:07 +00:00 committed by Cédric Deltheil
parent 7e64ba4011
commit a0715806d2
1 changed files with 8 additions and 6 deletions
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14
src/refiners/foundationals/latent_diffusion/image_prompt.py
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@ -235,7 +235,7 @@ class PerceiverResampler(fl.Chain):
class ImageCrossAttention(fl.Chain): class ImageCrossAttention(fl.Chain):
def __init__(self, text_cross_attention: fl.Attention, scale: float = 1.0) -> None: def __init__(self, text_cross_attention: fl.Attention, scale: float = 1.0) -> None:
self._scale = scale self._multiply = [fl.Multiply(scale)]
super().__init__( super().__init__(
fl.Distribute( fl.Distribute(
fl.Identity(), fl.Identity(),
@ -263,17 +263,20 @@ class ImageCrossAttention(fl.Chain):
ScaledDotProductAttention( ScaledDotProductAttention(
num_heads=text_cross_attention.num_heads, is_causal=text_cross_attention.is_causal num_heads=text_cross_attention.num_heads, is_causal=text_cross_attention.is_causal
), ),
fl.Multiply(self.scale), self.multiply,
) )
@property
def multiply(self) -> fl.Multiply:
return self._multiply[0]
@property @property
def scale(self) -> float: def scale(self) -> float:
return self._scale return self.multiply.scale
@scale.setter @scale.setter
def scale(self, value: float) -> None: def scale(self, value: float) -> None:
self._scale = value self.multiply.scale = value
self.ensure_find(fl.Multiply).scale = value
class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]): class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
@ -335,7 +338,6 @@ class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
@scale.setter @scale.setter
def scale(self, value: float) -> None: def scale(self, value: float) -> None:
self._scale = value
self.image_cross_attention.scale = value self.image_cross_attention.scale = value
def load_weights(self, key_tensor: Tensor, value_tensor: Tensor) -> None: def load_weights(self, key_tensor: Tensor, value_tensor: Tensor) -> None: